Cornell University

NIH Research Projects · FY 2025 · 2025-07

SUMMARY From insects to mammals, seminal fluid proteins (Sfps) significantly affect the reproductive physiology of mated females, the storage and release of sperm inside females, and (in mice) even the phenotype of progeny. Males abnormal for specific Sfps are sterile or subfertile, including in humans. Some Sfps bind tightly to sperm; others are free in seminal plasma. Yet despite their importance in reproduction, little is known about exactly how Sfps act to influence the female or the behavior of sperm in females. Importantly, many Sfps evolve rapidly, consistent with roles in molecular/evolutionary sexual conflicts. Understanding functional constraints on the evolution of Sfps and the proteins with which they interact in females will guide future investigations into Sfp actions in human fertility. We will combine molecular genetic and functional approaches to investigate: (1) how Sfps interact with female molecules to elicit reproductive responses and (2) how Sfps associate with sperm to mediate their effects, as well as how both types of function have evolved. We will investigate these questions using Drosophila, a premier genetic model system for dissecting Sfp function, with extensive resources for evolutionary comparisons. Importantly, Drosophila Sfps have many molecular and phenomenological parallels to those of mammals. Aim 1 focuses on ovulin, which stimulates ovulation by inducing neuronal octopaminergic signaling. This signaling regulates muscle contraction in the female reproductive tract, relaxing the oviducts and increasing ovulation rate. Using genetic screens and signatures of protein-protein coevolution, we have identified strong candidates for the female’s receptor for ovulin (OvR). We will test these for ovulin binding and then determine OvR localization, to pinpoint the site of ovulin action. We will then examine how well different species’ ovulins mediate ovulin action and OvR binding, to elucidate the evolution of their function. In Aim 2 we will focus on seminal proteins that bind to sperm, which we have identified by their coevolution or by proteomic methods. Our recent data show that Sfps prime sperm for binding to the critical Sfp called Sex Peptide. We will ask which seminal proteins function within this priming pathway and which act independently of that pathway. We will also investigate whether female secretions are also involved in priming. Finally, we will determine the extent to which the functions of a subset of sperm-bound Sfps are conserved across related Drosophila species. Elucidating how Sfps interact with and affect the female at the molecular level, as well as how these interactions evolve, is important for understanding and diagnosing Sfp-based infertilities, in considering strategies for assisted reproductive technologies that would benefit from inclusion of critical Sfps, and for developing new ways to control dipteran insects that transmit serious diseases like dengue, Zika, and malaria.

NSF Awards · FY 2025 · 2025-07

The objective of this project is to analyze the structural and algorithmic characteristics of high-dimensional probability models encountered in statistical inference and physics. Fundamental questions regarding the feasibility of extracting information from noisy data and executing efficient computations on it can be formulated within the framework of spin systems—models comprising a large number of locally interacting variables defined on a graph. The emergent global behavior of such systems provides insights into the statistical and computational limits of inference. Notably, varying the interaction strength can induce phase transitions, leading to significant reconfigurations of the probability mass. These transitions often delineate the boundary between algorithmically tractable and intractable regimes. The project aims to characterize the computational relevance of these transitions, devise efficient algorithms for the tractable phase, and establish lower bounds on algorithmic performance in the intractable phase. This project will focus on a class of canonical probabilistic models such as variants of the Ising model, mean-field spin glasses and perceptron models of neural networks. A primary objective is to design and analyze efficient algorithms for sampling from the associated Gibbs distributions and for finding lowest energy (or ground state) configurations. A second objective is to examine the concentration properties and possibly chaotic behavior under perturbation of the Gibbs measure. These aspects will be studied in relation to their algorithmic and statistical implications, with the aim of understanding how the geometry of the underlying energy landscape influences computational feasibility and inference performance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-07

This project focuses on the design of secure account management tools and frameworks, advancing the science of digital account security and fostering safer digital ecosystems for people who face targeted attacks. Online accounts increasingly play a central role in people’s wellbeing, acting as the gateway to email, finances, social networks, geographic locations, and even domestic home environments. However, a growing amount of evidence suggests that people who face heightened threats to their digital safety are often targeted by abusive adversaries; adversaries who bypass trusted authentication mechanisms for unauthorized access to victim accounts, enabling further harassment, surveillance, and control. The research agenda has four aims. The first will lead to a better understanding of experiences of targets such attacks, support professionals, and abusive adversaries through qualitative, stakeholder investigations. The second will complement this knowledge by cataloguing existing management tools and emerging authentication mechanisms as provided by online services via new forms of measurement studies and new framework for analyzing the abusability of account management tools. The third will offer new designs of account management tools to improve detection and investigation into illicit accesses, refining them via design provocation studies with relevant stakeholders. The fourth will create new empirically-grounded interventions for the targets to restore their digital safety, and the perpetrators of attacks to deter technology abuse at its source. The intellectual contributions of the work include: 1) new analytical frameworks for assessing illicit client-side access to accounts; 2) new frameworks for reasoning about UI-bound attacks and the abusability of tools; 3) new design patterns for account security management that can be applied widely; and 4) new direct interventions with both the targets of such attacks and abusive adversaries. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

A fundamental challenge in environmental science is applying the knowledge that researchers discover at a particular location or time to understanding phenomena occurring at other times and/or locations. In the traditional approach, environmental scientists collect field data and perform experiments at, for example, a particular river basin, and then repeat this process at a different time and/or location to see whether their conclusions generalize. This approach is rigorous, but limited because it is time-, labor-, and cost-intensive; thus there exists relatively sparse ground-collected data across the planet. Another challenge is that intensifying human activities amplify the rates of change in conditions per location and time, so knowledge discovered in the past will likely fail to predict outcomes in the future. The challenge of predicting and ameliorating the effects of environmental change disproportionately affects under-resourced communities, including those most vulnerable to environmental changes that lead to food insecurity and hence greater socioeconomic instability. Traditional Artificial Intelligence (AI) approaches cannot resolve this challenge because they require extensive human input, for example due to the need for labeling ground-collected data or other data layers, such as high-resolution satellite imagery. In this project, on-the-ground human observations and labels are replaced with AI-based discovery from abundantly available, mostly unlabeled visual data, such as that collected from a combination of satellites and other devices. This research proposes a paradigm shift that enables low-cost scaling across many types of images in order to lower the barrier to access of this scientific process. In the process, a novel AI framework is introduced that combines multiple data sources to automatically discover interpretable scientific hypotheses about the cause of ecosystem changes. Together, these approaches will accelerate the ability to identify solutions for the increasing environmental issues faced across our planet. The goal of this project is to develop and validate an AI framework that can use a broad array of image data collected using different sensing modalities (e.g., low-resolution satellite, drone, and internet-posted images) to automate and accelerate the generation of interpretable environmental scientific hypotheses at a planetary scale. An example might be correlating the spatiotemporal prevalence of certain invasive or disease-causing species with presumed causal factors present in the environment. The proposed framework integrates new techniques into foundational models for satellite imagery that can choose intelligently among sparsely-labeled data from different sensor modalities, optimizing between cost and accuracy trade-offs. By coupling this model with self-improving large language models that can both receive and provide interpretable feedback and hypotheses to researchers, this approach goes beyond black-box feature learning, the current state-of-the-art in computer vision. This proposed model will be applied and validated on the task of detecting submerged aquatic vegetation. This task poses a number of technical challenges (e.g., waves, turbidity, weak spectral signal through water) that are more difficult than detecting objects on land surface. Success in this pilot project will demonstrate that this type of model can be easily applied to the terrestrial environment and to tackle even greater grand challenges in environmental science. This award is co-funded by the Directorate for Computer and Information Science and Engineering and by the Directorate for Biological Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

With the support of the Chemical Synthesis (SYN) program in the Division of Chemistry Professor Song Lin of Cornell University is developing new chemical reactions that form nitrogen–nitrogen (N–N) bonds. N–N bonds are frequently present in useful organic compounds such as bioactive natural products, medicines, agrochemicals, and polymer materials. However, traditional approaches to construct N–N bonds are significantly limited by the types of products that they can provide. The Lin lab aims to develop a new reagent that can selectively couple two molecules containing amines, which are readily available species from feedstock chemicals and naturally occurring compounds. The long-term objective of this research program is to facilitate the development of new medicines, agrochemicals, and light-responsive materials. The project lies at the interface of organic synthesis, medicinal chemistry, and physical organic chemistry. Therefore, it is also well suited for the education of trainees including graduate and undergraduate students. In addition to contributing to the advancement of scientific knowledge and improving fine chemical preparation, the project will also exert broader impacts on the training of students through collaboration with other laboratories with complementary expertise. Professor Song Lin is studying the development of reaction methodologies using novel hypervalent iodine agents for the selective coupling of amines. N–N bonds are prevalent in bioactive natural products, medicinal agents, agrochemicals, and functional materials. An efficient and modular approach to synthesizing N–N containing molecules is the direct coupling of two N-containing fragments. Currently available protocols for N–N coupling predominantly focus on amides, imines, or arylamines as substrates. The direct coupling of unactivated aliphatic amines would drastically improve the synthetic scope of this bond disconnection strategy but such transformations remain elusive. To this end, the Lin lab will develop hypervalent iodine reagents with labile ligands to promote N–N coupling reactions to access important target products, including natural products, medicinal agents, and photoresponsive materials. Finally, the Lin lab will develop experimental modules on the basis of proposed N–N coupling reactions to synthesize photoresponsive compounds and use them in outreach events with high school students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

Job search and employment play a critical role in determining the success of individuals recently released from incarceration (IRRIs) to reenter their communities. However, as a stigmatized, resource-lacking population, IRRIs are at risk of experiencing unique and highly challenging stressors in their job search and employment, as well as to respond to the resulting strain in maladaptive ways (e.g., substance use), thus potentially increasing the odds of recidivism. To address these risks and enhance the odds of reentry success, there is a need to better understand the challenges experienced by IRRIs as they seek gainful employment. Accordingly, the project team is conducting interviews with IRRIs at various stages of their reentry, as well as with those working with them (e.g., halfway house directors and counselors), to explore the unique aspects of IRRI job search and employment. Informed by these interviews, the project team also aims to test how a combination of different job search tactics, individual background factors, and situational factors may combine to explain job search and employment outcomes. The findings will be used to inform efforts aimed at helping IRRIs successfully transition to employment (e.g., guiding the more effective investment of reentry resources), as well as to facilitate the development of more efficacious interventions that mitigate risky behaviors and reduce recidivism. This project aims to shed light on the adaptive and maladaptive coping tactics adopted by IRRIs in response to the stressors and strain they experience in their job search, how such factors impact their job search success, and how individual and organizational search-related resources and demands can impact employment outcomes. The project involves two studies. Building on conventional models of job search and the literature on community reentry, Study 1 is an exploratory, qualitative study in which we will interview up to 30 IRRIs (at various reentry stages) and 10 reentry professionals with the aim of developing a model of job search predictive of IRRI job search and employment outcomes. Study 2 aims to quantitatively test the hypothesized relationships embedded in the model emerging from Study 1 by collecting longitudinal field survey data from 200 IRRIs in three evenly spaced waves over their first four months of post-incarceration reentry. Results from the research will contribute to the understanding of experiences and challenges of IRRIs as they search for employment as part of their community reentry, as well as to the development of more efficacious policies and interventions to mitigate risky behaviors among such individuals and reduce their odds of recidivism. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This project constructs a new dataset from county land-use records. The dataset includes information on land records from 20 counties spread across the United States over 40 years. It includes data on the Federal Housing Administration (FHA) insured and Veteran’s Administration (VA) guaranteed mortgages in these counties. The data will be publicly available to academics, decision makers, community organizations, and individuals who want to evaluate the impact of these two important federal programs. The team is also providing an initial analysis of these data to describe and analyze the spatial and demographic patterns of federal mortgage insurance. This analysis leverages individual-level borrower data and address-level property information to examine how these programs affected homeownership, wealth, and neighborhood outcomes in both urban and rural counties with different population characteristics. The research findings have the potential to improve the functioning of mortgage markets, thereby enhancing the well-being of U.S. households. The award is jointly funded by the NSF programs in Economics, Sociology, and Human-Environment and Geographical Sciences (HEGS). The project creates a new data resource that provides the most extensive and granular data on the demographic and spatial distributions of FHA-insured and VA-guaranteed mortgages to date. Because the data include information on issued mortgages, the data allow scientists to consider the results of enacted policy rather than simply examining government agency reports and correspondence. The team is collecting and geocoding data on roughly 280,000 mortgages. They are using the data and econometric methods to provide detailed descriptive statistics on the recipients of government-insured mortgages. They also use the data to test hypotheses about the effects of FHA and VA loans on neighborhood composition, including information on differences across population groups and neighborhoods. The project advances knowledge in economics, sociology, and geography. The broader impacts include access to data and enabling science-informed discussions about issues that affect wealth accumulation, neighborhood outcomes, intergenerational mobility, and demographic differences. The project also involves students and early career researchers in data collection and analysis. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This award supports a collaborative research project to advance understanding of articular cartilage, the soft tissue covering the ends of bones in joints. The project will examine how cartilage responds to physical forces relevant to daily activities like walking and jumping. While much is known about how cartilage resists compression, less is understood about how it responds to shear forces, during twisting and sliding. Recent findings suggest the collagen fiber network, which provides structural support, is close to a mechanical phase transition, where small structural changes can significantly impact mechanical properties. By integrating theoretical modeling, simulations, and experiments, the researchers will study how osmotic pressure and different types of mechanical stress influence cartilage structure and function. Understanding these processes could lead to better osteoarthritis treatments, improved tissue engineering and repair strategies, and bio-inspired materials for various other applications. This project will support education and workforce development by training students in biomechanics equipping them with valuable research skills. Additionally, the project will provide mentorship opportunities through workshops and outreach programs and facilitate public outreach activities. This award supports a collaborative research project to advance our understanding of articular cartilage, a living load-bearing tissue that can endure decades of mechanical stress. While its compressive behavior is well understood, the mechanisms governing shear resistance and its interaction with compression remain unclear. Recent findings suggest that collagen fibers form a percolating network near a mechanical phase transition, where small structural changes significantly alter mechanics. Prior studies examined small strains, but cartilage undergoes large deformations, reaching strains up to 40 percent under normal and extreme conditions. This project looks to develop next-generation rigidity percolation models to investigate cartilage mechanics under physiologic and super-physiologic loading. It plans to integrate experiments, theory, and simulations in an interactive feedback loop to examine how osmotic stress, mechanical loading, and collagen network reorganization shape tissue function. Rigidity percolation models intend to capture large deformations and multi-directional loading, providing a predictive framework for mechanical phase transitions. Experimental tests seek to validate and refine these models, while imaging looks to track fiber alignment and network adaptation. This research intends to reveal how mechanical phase transitions regulate cartilage response under extreme condition, informing cartilage repair and tissue engineering. The project will train students in biomechanics, and modeling while facilitating science communication and public outreach. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This research examines how visions of a global “information age” have changed ways of conceptualizing and experiencing human potential. The dissertation study uses three case studies to examine the interplay between embodiment and technoscience. This work addresses broader contemporary concerns about how information technologies have intimately changed the way people understand themselves and their connection with their surroundings. The results of this project are communicated through conference presentations, journal publications, and undergraduate teaching. Combining state archives with records from published books, journals, media reports, laboratory reports, and memoirs, the project examines how the information revolution also entailed a transformation of human bodily understanding. The study asks: 1. How did medico-scientific and technological practices generate new visions of human capability and potential, and how did these intersect with visions of technoscientific modernization? 2. How have theories such as information theory, systems science, and cybernetics become localized and how do they interact with vernacular medical knowledge to shape new understandings of the human body as an informational entity? To address these questions, the project combines theoretical frameworks from Science and Technology Studies with archival research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This I-Corps project is based on the development of an engineered strain of a marine bacterium, Vibrio natriegens, that has been developed to serve as a host organism to scale biotechnology and bioproduction. Microbes that are commonly used today as host organisms for biotechnology and bioproduction are chemically competent, meaning they are chemically treated to take in DNA. Though commonplace, this process makes cells fragile, requiring investments in equipment and infrastructure to utilize them. In contrast, Vibrio natriegens is naturally competent, meaning it will take in DNA without any chemical intervention. Vibrio natriegens is the fastest growing organism on Earth and does not cause disease. This bacterial strain has been engineered to enhance scalability and ease of handling to allow for integration with fully automated systems. This scalability and each of handling may accelerate modification and testing and improve protein biomanufacturing as well as the ability to synthesize proteins of interest. These capabilities may shorten the Design-Build-Test-Learn cycle and improve the speed and simplicity of workflows. With increased speed in these processes, the time and cost of pharmaceutical and vaccine development may be reduced, and new biological solutions to current problems may be developed more rapidly and easily. This I-Corps project utilizes experiential learning coupled with first-hand investigation of the industry ecosystem to assess the translation potential of an engineered strain of Vibrio natriegens as a chassis for scalable molecular biology and bioproduction. Vibrio natriegens expresses a high rate of natural competency, meaning it will organically take in DNA without chemical or other treatment. This is different from currently used E. coli strains that must be chemically treated every time DNA is introduced into the cell. Utilizing a genomic insertion of the TfoX master competency regulator gene combined with a specifically designed growth media, a strain of Vibrio natriegens was developed that can be transformed with DNA without the need for special equipment, which means transformation can be completed at room temperature with no heat shock, shaking, media transfers, or incubation. This technology may lower the cost of synthetic biology by several orders of magnitude and decrease the hands-on time to single minutes. In addition, due to Vibrio natriegens rapid growth rate, an experiment starting with DNA isolation to production of single colonies may be completed in a single day. Recombinant proteins can be rapidly tested and automated at large scales with ease. Further, cells have been shown to remain competent after transformation, allowing for repeated transformations of the same bacteria. This technology may enable more rapid genetic engineering and protein production. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-06

This award supports U.S. based students and early career researchers in attending The William Rowan Hamilton Geometry and Topology Workshop 30 June–4 July 2025 at the The Hamilton Mathematics Institute, Trinity College, Dublin, Ireland. This five-day directed workshop will be a major international event, one of the largest ever assembled in the field of geometric group theory with 200 participants from around the world. There are 22 confirmed speakers, featuring many leaders of the field and allied areas, and ranging from senior figures to leading early-career researchers. The conference will present an unrivaled opportunity for researchers to interact with their peers from around the globe, will orient future research directions, will further US, Irish, and, European collaboration, will broaden access to mathematics, and will improve interdisciplinary collaboration. The first major theme of this workshop will be geometric group theory, with a focus on current areas of great interest including nonpositive curvature, automorphisms of free groups and related groups, and profinite rigidity. The second major theme of this workshop will be to elucidate connections between geometric group theory and other areas of mathematics. Examples include: low-dimensional topology for example via the work of Agol and Wise on the Virtual Haken Conjecture and cube complexes; geometry and number theory for example via the work of Reid on arithmetic lattices in rank-1 Lie groups; and computer science for example via the work of Lubotzky on high-dimensional expanders. The conference web page is at: www.maths.ox.ac.uk/groups/topology/william-rowan-hamilton-geometry-and-topology-workshop-celebrating-martin-bridsons-60th-birthday This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-05

The goal of my research program is to delineate the inner workings of membrane-associated protein complexes that are required to shape the form and function of tissues, and whose dysfunction leads to serious human diseases. We use a multi-disciplinary approach that combines deep genetic screens, cutting-edge transgenesis and live imaging in C. elegans, with biochemical assays of the homologous vertebrate complexes. We also collaborate with experts in structural biology and proteomics to reveal mechanistic details and binding partners of our purified complexes that we then further evaluate in vivo. Using this iterative approach, we have discovered two classes of proteins that act in opposition to regulate the predominant portal of entry into eukaryotic cells, clathrin-mediated endocytosis. The key player in this process, the clathrin adaptor complex AP2, is necessary for linking clathrin to the cell membrane to drive endocytosis. We discovered that NECAP family proteins recognize activated AP2 clathrin adaptor complexes that have been phosphorylated and return them to the inactive state, while endocytic initiators called muniscins/FCHo stimulate inactive AP2 complexes to adopt an intermediate conformation we have named ‘primed’. Together, these allosteric modulators of AP2 will enable us to investigate how cells select endocytic sites and cargo for internalization, while ensuring non-productive or inappropriate events are aborted. Recently, our studies to understand the physiological consequences of inactivating AP2-dependent endocytosis at the organismal level have opened new lines of research. In C. elegans, AP2 mutants exhibit a distinctive ‘cyst-like’ epidermal pathology. Through genetic screens, we have recently discovered mutant forms of the Inversin complex that mimic this phenotype. In humans, mutations in the Inversin complex cause lethal kidney cysts through unknown mechanisms, potentially linking what we see in our C. elegans model to the human disease. We have now isolated other mutations in the Inversin complex that reverse the cyst-like phenotype and will leverage these initial findings to determine the organization and output of this enigmatic complex. We find that ‘cyst’ formation also depends on the worm homolog of ASPP family proteins. Although poorly understood, mutations in ASPPs are clearly associated with heart, hair, and skin pathologies in mammals. Our biochemical and functional assays suggest ASPPs form membrane-associated arrays of protein phosphatase 1 (PP1). We will determine the molecular arrangement and activity state of this novel mechanism of phosphatase regulation using single-molecule imaging and optogenetic techniques. Both the ASPP and Inversin complexes localize to apical epidermal junctions; disrupting these complexes perturbs patterning of the apical extracellular matrix and underlying cytoskeleton. How these poorly characterized junctional complexes and membrane trafficking coordinate collagen-based and actomyosin networks remains an open question that we will address in the next five years by combining our established approaches with new ones, such as electron microscopy.

NSF Awards · FY 2025 · 2025-05

This research investigates how to increase the impact of health risk messages in naturalistic settings by considering the social contexts in which messages are received. Many health campaign and intervention messages are based on well-tested theories. Though effective in controlled experiments, communication intervention messages often fall short in real-world contexts. This project tests how adding social support elements, including encouraging messages and information aids, can enhance health message effectiveness, such as increasing people’s confidence in adopting risk mitigation behaviors. Study findings contribute to scientific progress and societal well-being by advancing knowledge in health communication and social support. The research provides insights into designing effective risk communication campaigns and interventions that account for social and contextual factors, offering a blueprint for improving health outcomes in the future. The project involves a three-phase intervention. In the first two phases, interviews and focus groups are conducted to guide the development of intervention materials. In the third phase, an online intervention delivers a strategic health video to participants to influence their beliefs and intentions, followed by social support text messages for two weeks. The study provides practical guidance for designing health risk communication campaigns and interventions that are not only scientifically grounded but also responsive to real-life social contexts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

Non-technical Abstract: All particles in our three-dimensional universe are classified as either fermions or bosons. However, in two dimensions, more exotic variants are possible. This project investigates anyons, emergent particles in two-dimensional materials that are neither fermion nor boson. The exotic properties of anyons are not only fundamentally fascinating but also hold potential for future fault-tolerant quantum computing. The research team searches for signatures of anyons by developing novel microscopic methods and exploring new classes of quantum materials. In collaboration with outreach programs, the team excites public interest in quantum physics through hands-on demonstration tours. The project also modernizes university experimental physics education by incorporating quantum information and machine learning methods, and it establishes an online platform to make two-dimensional material preparation more accessible to a broader research community. Technical Abstract: Anyons, predicted in topological systems such as the fractional quantum Hall effects, exhibit behaviors governed by their unique quantum statistics. Theory suggests that exchanging two abelian anyons results in a fractional phase shift, while exchanging two non-abelian anyons causes a unitary transformation. Non-abelian anyons feature highly degenerate ground states that are topologically protected from perturbations and can only be altered through particle exchanges, known as braiding, making them excellent candidates for fault-tolerant quantum computing. Although recent interferometry experiments have advanced in detecting abelian anyons, capturing signatures of non-abelian quantum statistics requires more than just extensions of current methods. In this project, the research team utilizes a scanning tunneling microscope (STM) to probe and manipulate anyons. This method allows the team to directly visualize anyon wavefunctions, reveal their abelian and non-abelian statistics, and gather valuable real-space information. More importantly, the project leverages STM-induced potentials to manipulate the position of anyons while conducting interferometry, facilitating the demonstration of non-abelian anyon braiding. Furthermore, the project investigates fractional Chern insulators—the zero-field counterparts of the fractional quantum Hall effect—using STM to elucidate the origins of their spontaneous topological properties and potentially enable access to anyons without the need for large magnetic fields. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

With support from the Chemical Structure and Dynamics (CSD) program in the Division of Chemistry, Professor Andrew Musser of Cornell University is using a new spectroscopic approach based on molecular vibrations to understand how light-matter interactions can tune the properties of molecules non-synthetically. When molecules are placed in carefully designed photonic structures that trap light, they often behave in surprising ways, forming new ‘polariton’ states that exhibit changes in charge and energy transport or chemical reactivity. These effects could transform photochemistry and catalysis, but they remain poorly understood and hard to predict. Professor Musser and his students will apply cutting-edge ultrafast laser-based techniques to a library of optical cavities in order to watch molecules inside move in real time and determine how their motions, which are fundamentally linked to reactive properties, are altered by the light-matter coupling. By isolating the unique dynamics of the polaritons, the team will establish how such photonic structures can be used to redirect molecular photochemistry without having to synthesize new molecules. Their studies could lead to new fundamental understanding of strong light-matter interactions and inspire new developments using polaritons for improved photocatalysts or light-emitting devices. The team will train multiple undergraduates in spectroscopy research and develop middle-school outreach programs focused on the nature of light and light-matter interactions. A core premise behind the idea of polariton-controlled (photo)chemistry is that strong light-matter coupling distorts molecular potential energy surfaces. This effect has never been detected. This project will use a sensitive ultrafast vibrational technique—impulsive vibrational spectroscopy—to capture the signatures of excited polaritons by launching and tracking coherent vibrational wavepackets on the ground- and excited-state potential energy surfaces. By incorporating optical control pulses, the team will distinguish any unique polaritonic wavepacket dynamics and infer the associated changes to the potential energy landscape. Comparing these measurements over systematically tuned microcavities, the team will develop empirical guidelines for how to maximize such polaritonic distortions and thereby alter the dynamics of coherent photochemical processes. The results will lay the photophysical foundation and identify the key photonic and materials control knobs to dial in polaritonic effects. This work will open an avenue to develop polaritonic (photo)chemical reactors that exploit light-matter interactions to redirect traditional reaction pathways, creating a valuable resource for the synthetic chemistry community. Students who participate in this project learn an array of relevant for high technology professions in the molecular sciences or photonics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

U.S. consumers have become accustomed to fast, inexpensive delivery directly to their doorstep. Yet few consider the work or the workers who are doing the deliveries. Parcel delivery is one of the fastest growing occupations open to people with only a high school education. It is also a physically demanding job with high injury rates. Delivery work relies on public roads and delivery workers are in direct contact with customers in their homes and businesses. As the so-called “last mile” delivery industry grows, there are concerns about the traffic accidents, congestion, and pollution that impact all citizens, in addition to worries about wages, job quality, and working conditions for workers. This study of last mile delivery drivers seeks to accurately describe the nature of the parcel delivery work, to assess the differences across organizations that employ or engage delivery workers, and to evaluate the advantages and disadvantages of new technologies such as electric vehicles, dashboard cameras, vehicle sensors and digital parcel tracking, and to evaluate the role of unionization. This research will examine differences among parcel delivery vendors and management systems to inform managers, customers, present and prospective delivery workers and regulators and assist in making better choices. The last-mile delivery industry offers a unique opportunity to compare different systems for organizing the same work and to consider how weather conditions impact working experiences. The project team is surveying a representative sample of last-mile delivery drivers in two regions in the U.S. that are comparable in geographic and labor market size and scale, encompass the full range of urban, suburban, and rural delivery routes, and offer similar non-delivery job opportunities, but differ in an important occupational hazard facing delivery workers – weather. The survey questions focus on working conditions, management practices, and worker attitudes and behaviors. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

This collaborative research project will address the growing mental health crisis that affects over 40% of graduate students by exploring how graduate program directors (GPDs) can make trauma-informed care a programmatic default. GPDs can shape departmental procedures, enact institutional policies, and disrupt power dynamics between faculty and students. In other words, GPDs are central to improving and sustaining graduate students’ mental health and wellbeing. As such, this project will construct a curriculum with GPDs to aid in implementing trauma-informed practices to a large audience of GPDs. The project outcomes will support the improvement and development of proactive interventions to support positive mental health and wellbeing in graduate engineering programs. Improving the systems that support graduate student mental health in engineering programs will enhance recruitment and retention at all levels of engineering education, which in turn addresses the national need of training engineers to address grand challenges. The project results will also raise awareness of graduate mental health in engineering programs by preparing faculty in leadership positions to change the climate in graduate programs directly. These changes will serve as evidence-based models that increase the adoption of practices to support mental health and promote student wellbeing. This project will examine the mental health crisis by focusing on the role of GPDs in integrating frameworks of care that consider the full range of traumas graduate students have experienced or could experience. The results of this investigation will characterize the roles of engineering GPDs and inform the development of methods to train a community of GPDs to implement evidence-based practices that foster care. This project will use a two-phase research design to address three research questions (RQ): RQ1: What are the characteristic roles of engineering graduate program directors in fostering cultures of care in their programs? RQ2: How do the systemic structures within higher education impact engineering graduate program directors’ implementation of trauma-informed frameworks of care? RQ3: What professional development program features can support engineering graduate program directors’ perceived ability to integrate trauma-informed frameworks of care in their approach to supporting graduate students? Phase 1 will leverage sequential mixed methods through a national survey followed by semi-structured interviews to characterize the roles of engineering GPDs and how programs leverage care practices. Phase 2 will build on these characterizations to collaboratively develop an evidence-based professional development framework for creating trauma-informed systems of care within engineering graduate programs. We will integrate a group coaching professional development approach with collaborative inquiry to explore the lived experiences of the GPDs participating and enable their attempts to foster care in their programs. The research design will expand existing theories for implementing trauma-informed frameworks of care to promote positive mental health and wellbeing within engineering graduate education. The results will inform the practices that faculty and instructors can use to realize the broad impact of trauma and recognize students experiencing, coping, and reacting to trauma. Four broader impacts will emerge from this project: (1) A characterization of GPD roles in engineering that researchers can use to accelerate other educational innovations in graduate engineering education, (2) A prototype developed and disseminated in collaboration with GPDs and an advisory board containing licensed professional counselors that can empower and enable GPDs to respond to students experiencing trauma and to minimize the occurrence of new trauma, (3) Structural changes to support students that have or will experience trauma, so they can return to deep learning and increase their likelihood to persist, and (4) Faculty who can better support systemically minoritized students through the implementation of care practices that respond to students’ identity-driven experiences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-05

The Householder Symposium XXII will be held June 8-13, 2025, at the Statler Hotel on the campus of Cornell University in Ithaca, NY. The symposium gathers the world's most active researchers in numerical linear algebra once every three years to review advances in the field, present recent theoretical and practical results, and assess where the field is headed in the future. A hallmark of the Symposium is its intimate atmosphere, which promotes close personal interaction and the free exchange of ideas. Many researchers in numerical linear algebra consider this gathering to be the most important and influential meeting in the field, reflecting its rich tradition dating back to the original "Gatlinburg" conferences founded by Alston Householder in the 1960s. This proposal supports the travel costs of 12 junior U.S. researchers to the Householder Symposium XXII. The funds will provide essential support for U.S. graduate students and recent Ph.D.s who might otherwise be unable to attend. By enabling full participation for promising junior members of the U.S. numerical linear algebra community, these NSF funds ensure that early-career scientists are introduced to leading experts in the field and have opportunities to showcase their cutting-edge work. The Householder Symposium has traditionally been a gateway conference into numerical linear algebra, making the attendance of junior scientists vital both for their professional development and for maintaining the robust U.S. competitiveness in this critical discipline. Key topics to be emphasized at Householder XXII include the solution of large systems of linear equations, eigenvalue problems, preconditioning, perturbation theory, least squares, integral equations, and diverse applications in scientific and engineering computation, such as control, systems and signal processing, data mining, data compression, and bioengineering. Advances in numerical linear algebra have had profound effects on numerous scientific and technological fronts, including web search engines, the global positioning system, high-speed supercomputing algorithms, machine learning, and data science. Ensuring that the next generation of researchers actively participates in the conference will sustain the innovative momentum needed to address future challenges in science and engineering. More information about the conference is available at the website: https://householder-symposium.github.io. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

Whole Genome Sequencing (WGS) is a powerful tool for uncovering genetic variants linked to diseases, understanding evolutionary processes, and tracing population histories. Given that over a million human genomes have been sequenced to date, the sheer volume of data requires advanced computational solutions for efficient analysis. Our research utilizes the Genotype Representation Graph (GRG) to improve the performance of WGS data analysis significantly. By optimizing this data structure and using modern parallel computing architectures and techniques, we aim to reduce the time required for complex genomic analyses and enable the fast and efficient processing of large datasets such as housed in the UK Biobank. This project aims to develop tools and infrastructure that will enable researchers to advance our understanding of human genetics and improve the accuracy of population genetic studies, which will ultimately contribute to better health outcomes and greater scientific knowledge. In addition, this approach to representing large, complex data sets and manipulating them effectively will serve as a proxy for modern computing approaches, to guide the design of advanced parallel computing architectures and techniques. This research focuses on improving the efficiency of Whole Genome Sequencing (WGS) data analysis through two main objectives. The first objective is to optimize the Genotype Representation Graph (GRG) for modern parallel computing architectures, particularly GPUs, to handle the dynamic nature of genomic computations. Additionally, a matrix abstraction of the GRG will be developed, to enable efficient computation on architectures beyond GPUs by utilizing sparse matrices for near-linear scaling on distributed memory machines. The second goal is to use the improved GRG to perform accurate Ancestral Recombination Graph (ARG) inference, a critical step in population genetics. By implementing and testing these approaches on the large-scale UK Biobank data, the scalability and accuracy of the novel methodologies will be demonstrated. This interdisciplinary project will combine high-performance computing advances with innovative data structures to answer key questions in population genetics and provide insights for future high-performance systems in the post-Moore’s Law era. This award is co-funded by the Directorate for Computer and Information Science and Engineering and by the Directorate for Biological Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

This grant supports a workshop on the formalization of human factors into computational models. The workshop will convene 40 participants with expertise in human factors, behavioral economics, cognitive neuroscience, social psychology, human-centered design, control theory, robotics, formal methods, and autonomous systems. Through presentations and discussions, participants will articulate the state-of-the-art capabilities in these fields while identifying research gaps and challenges related to the development and implementation of theoretical and computational models of human factors. An interdisciplinary approach that facilitates a common language and objectives across disciplines is key to this endeavor. The results of this workshop will advance the fields of robotics, dynamics, and control by identifying advanced capabilities and technologies that could arise from the use of models that methodologically capture non-trivial human behaviors. The results of this workshop will help advance the science of autonomous systems and could broadly impact research directions and capabilities in robotics and control. After the workshop, a report will be generated to summarize the findings, which will be shared with the general research community. Despite extensive work in the development of autonomous dynamical systems, system capabilities are fundamentally limited by the challenges associated with effective, accurate, and computationally tractable models of human factors. To move the field beyond simplistic assumptions of human behavior that limit system capabilities, new approaches are needed to more faithfully capture the complexities humans bring to autonomous systems. The activities of this workshop are designed to bring together systems and controls researchers who share the challenge of translating important concepts from behavioral economics, social psychology, human factors, and human-centered design into frameworks amenable to computation and control of dynamical systems. Workshop activities will help participants develop and employ a common language that facilitates a meaningful exchange of ideas across disciplines to articulate better a roadmap for addressing research gaps and challenges that exist in formalizing human factors into computational models. Identifying these challenges is essential for advancing the field of systems and control. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

Tissue morphogenesis, or the development of tissue shape, is driven by a combination of cellular forces, mechanical constraints, and changes in extracellular matrix (ECM) composition and architecture. This combination of parameters is difficult to study. It is still unknown how mechanical forces, including external forces applied to the tissue and forces within the tissue due to those external forces, influences the evolution of tissue shape. The main hypothesis of the project is that cells can be directed to create a desired tissue shape, structure, and size by applying controlled dynamic mechanical signals. These mechanical signals can be applied and removed on-demand, and they can quickly spread long distances. The application of a force in the tissue can be controlled precisely using robotic micromanipulation tools. The goal of this project is to use computational design tools to instruct robotic manipulation in a way that will uncover the mechanical laws and physical forces that drive connective tissue morphogenesis. The results will enable the use of mechanobiology to engineer tissues. Novel materials will be developed for undergraduate and graduate courses, and the next generation of engineers and scientists will be trained in microfabrication, cell biology, continuum mechanics, magnetics, materials science, and robotics. This project aims to uncover fundamental mechanistic principles of connective tissue morphogenesis and introduce computational design tools that would instruct the use of robotic micromanipulation tools with the goal of harnessing mechanobiology to engineer architected tissues. Computational models based on continuum mechanics will be developed to predict force generation during the deformation and shaping of engineered microtissues. These models will recapitulate the concurrent alignment, rearrangement and deposition of the ECM. Imaging and mechanical characterization techniques will spatiotemporally map tissue rheology and architecture, generating a dataset that will be used to validate the computational models. Combined experimental and computational work will aid in the discovery of the physical principles of mechanosensitive cell migration in morphing and structurally remodeling fibrous tissues. The integrated model will predict the evolution of cell movement and ECM architecture along with the tissue morphology under dynamic mechanical loading. The integrative model that recapitulates both ECM remodeling and cell migration will guide tissue folding. Under robotic micromanipulation informed by the model, bilayer tissues will fold into 3D tissues with pre-defined shapes and internal architectures. The work has scientific, societal, and educational impact. This work has the potential to uncover multi-cellular organization principles that drive developmental patterning, wound healing, and pathological responses such as fibrosis that involve cell migration and ECM remodeling in fibrous tissues. The framework will guide the engineering of a variety of complex biological tissues towards regenerative medicine, creation of living machines, and development of tissue culture systems for pharmaceutical screening. This collaborative US-Swiss project is supported by the US National Science Foundation (NSF) and the Swiss National Science Foundation (SNSF), where NSF funds the US investigator and SNSF funds the partners in Switzerland. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

This award supports a 2-day workshop, “Identifying and Bridging Gaps in Laboratory Astrophysics,” to be held in conjunction with the 246th American Astronomical Society meeting in Anchorage, Alaska. The workshop will be a timely forum for laboratory astrophysics researchers and stakeholders to discuss current needs to address frontier astrophysical problems, promote collaboration, and articulate community priorities to inform the mid-Decadal Review of Astronomy and Astrophysics. Laboratory astrophysics uniquely combines fundamental scientific exploration with practical applications, offering an excellent opportunity to build a skilled workforce that can contribute to both academic research and industry. The workshop will emphasize workforce development by providing early-career researchers with opportunities to interact with established researchers and contribute to discussions about the future direction of the field. This will help generate ideas to strengthen the pipeline of skilled professionals that is essential to maintaining the global leadership of the United States in science and technology. The workshop seeks to better coordinate laboratory astrophysics efforts with the priorities of modern astronomy as articulated in the 2020 Decadal Survey of Astronomy and Astrophysics (Astro2020). By bringing together data “producers” and “consumers,” the workshop will enhance interdisciplinary research efforts while addressing challenges and opportunities identified by the Laboratory Astrophysics Task Force convened by the Astronomy and Astrophysics Advisory Committee in response to Astro2020. Discussions will center around critical topics such as planetary atmospheres, stellar evolution, high energy astrophysics, and diffuse media, ensuring that laboratory astrophysics remains responsive to the evolving needs of observational astronomy. This community-driven effort will yield key insights and actionable ideas for effectively supporting laboratory astrophysics to maximize the scientific output and impact of federally funded observatories and missions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

This I-Corps project focuses on the development of a mixture analysis technology using enhanced nuclear magnetic resonance spectroscopy. The ability to accurately analyze complex chemical mixtures is critical in industries such as pharmaceuticals, natural product synthesis, and forensic science. Traditional methods often struggle with overlapping signals and require extensive manual interpretation, leading to inefficiencies and high costs. This solution introduces an innovative approach that improves the resolution and precision of mixture analysis, enabling users to identify components with greater accuracy and speed. By enhancing chemical identification, this technology reduces the risks associated with product recalls, regulatory compliance issues, and inefficiencies in quality control. The potential benefits extend beyond industry applications to fields such as environment monitoring and public safety. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a novel analytical method that combines Wavelet Packet Transform with super-resolved proton nuclear magnetic resonance spectroscopy. This method provides superior resolution for overlapping signals, enabling unsupervised analysis without prior knowledge of mixture composition. Unlike conventional techniques, this approach is highly resistant to noise, making it suitable for challenging experimental conditions. By integrating this advanced signal processing method with existing quality control workflows, the technology offers a scalable and user-friendly solution for industries requiring precise chemical analysis of mixtures. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

Machine learning has made significant advancements in tasks like image recognition and click prediction, enabling applications such as self-driving cars and personalized social media feeds. However, when machine learning predictions are deployed to make decisions, small errors can have unintended consequences. In feedback loops, these errors can compound, leading to problems in safety, bias, and performance. This project aims to address this challenge by developing algorithms for reliable decision-making in complex systems, ensuring that the benefits of machine learning are realized while minimizing its risks. By understanding how feedback loops work, we can unlock the full potential of machine learning to improve outcomes in areas like weather prediction, recommendation systems, and human-robot collaboration. Our research program will develop innovative solutions that benefit society as a whole, with a focus on developing theory and algorithms that have direct impact on these applications. In addition, an integrated education plan will promote computer science education and inspire the next generation of innovators. Hands-on projects and interactive modules will introduce high school students to exciting topics like weather forecasting, balloon control, and robotics. The project will leverage ideas and techniques from online optimization, control theory, system identification, and reinforcement learning to answer the following questions: How should decision algorithms make use of possibly unreliable machine learning predictions while ensuring good outcomes? How can we reliably predict the long-term impacts of decisions with models learned from temporally correlated data? How do we adaptively make decisions while learning about initially unknown impacts? The algorithmic and theoretical frameworks will be developed in tandem with applications in weather prediction, autonomous aerial navigation, recommendation systems, and human-robot interaction, along the following three thrusts. First, consider decision-making with ML predictions. Reliably leveraging unreliable predictions requires accounting for potential errors to guard against bad outcomes. The researchers will first develop algorithms that robustly guarantee performance and safety while benefiting from predictions when they are accurate and then use decision performance as a metric to evaluate prediction quality. Second, consider learning models of impacts. Understanding the long-term impacts of decisions on individuals is crucial, and yet human activities are non-stationary, correlated, and partially observed. The researchers will develop reliable learning algorithms for data arising from such processes, with a particular focus on finite sample uncertainty quantification and bounded sample complexity. Third, consider sample-efficient reinforcement learning: Adaptive decision-making requires simultaneously learning from data while making decisions. The researchers will develop model-based algorithms which can operate even in partially observed settings, such as the non-stationary user behaviors important for applications like recommendation and human-robot collaboration. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NIH Research Projects · FY 2026 · 2025-04

PROJECT SUMMARY Serotonergic psychedelics are best known for altering perception, cognition, and mood. Recently, there has been a resurgence in clinical trials investigating psychedelics for their potential in treating neuropsychiatric illnesses, such as substance use disorder. Psilocybin-assisted therapy, for example, holds promise in treating alcohol, opioid, or stimulant addiction. It has received attention for showing positive, long-lasting effects after only a single treatment. Still, it is unknown how psilocybin acts on the brain immediately after administration to begin altering neuronal processing. This proposal aims to determine psilocybin’s cellular and microcircuit mechanisms of action in the mouse medial frontal cortex, with a specific focus on GABAergic interneurons and soma-dendrite coupling. Interneurons in the frontal cortex express serotonin receptors and likely respond to psilocybin to mediate changes in cortical information processing. Meanwhile, soma-dendrite coupling controls the efficiency of information flow in pyramidal neurons, thereby governing spike output, and it is associated with cognitive processing. In the medial frontal cortex, psilocybin-driven changes in microcircuit activity and soma-dendrite coupling may therefore underlie the cognitive state that accompanies a psychedelic experience. More importantly, these cellular and microcircuit-level changes may be linked to psilocybin’s therapeutic effects. In Aim 1 of this proposal, we will use two-photon calcium imaging to determine how psilocybin influences the activity of three dendrite-modulating interneuron populations in the medial frontal cortex. Additionally, we will examine whether the observed activity changes are mediated by specific serotonin receptors expressed in these interneuron populations. In Aim 2, we will use multi-plane calcium imaging to determine how psilocybin influences soma-dendrite coupling in pyramidal neurons of the medial frontal cortex. We will also perturb the activity of the three dendrite-modulating interneuron populations to determine their role in shaping soma-dendrite coupling during psilocybin action. The proposed experiments are designed to elucidate how psilocybin shapes cortical computations. In doing so, this proposal will advance our understanding of the neurobiology of psychedelics, specifically in the circuits that are important for executive function and impulse control, which will lead to improved treatments for addiction.